





import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from datasets import Dataset, load_dataset

import transformers
from transformers import (
    CONFIG_MAPPING,
    MODEL_FOR_MASKED_LM_MAPPING,
    AutoConfig,
    AutoModelForMaskedLM,
    AutoTokenizer,
    DataCollatorForWholeWordMask,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process


_lowerCamelCase   :							Any    				=   logging.getLogger(__name__)
_lowerCamelCase   :							Optional[Any]    				=   list(MODEL_FOR_MASKED_LM_MAPPING.keys())
_lowerCamelCase   :							Optional[Any]    				=   tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)


@dataclass
class 		lowerCamelCase  :







							"""simple docstring"""




							UpperCAmelCase_     		=       field(
							    default=a_				,      metadata={
							        "help": (
							            "The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."
							        )
							    }				,      )
							UpperCAmelCase_     		=       field(
							    default=a_				,      metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(a_     )}				,      )
							UpperCAmelCase_     		=       field(
							    default=a_				,      metadata={
							        "help": (
							            "Override some existing default config settings when a model is trained from scratch. Example: "
							            "n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"
							        )
							    }				,      )
							UpperCAmelCase_     		=       field(
							    default=a_				,      metadata={"help": "Pretrained config name or path if not the same as model_name"}     )
							UpperCAmelCase_     		=       field(
							    default=a_				,      metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}     )
							UpperCAmelCase_     		=       field(
							    default=a_				,      metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}				,      )
							UpperCAmelCase_     		=       field(
							    default=a_				,      metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."}				,      )
							UpperCAmelCase_     		=       field(
							    default="main"				,      metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}				,      )
							UpperCAmelCase_     		=       field(
							    default=a_				,      metadata={
							        "help": (
							            "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
							            "with private models)."
							        )
							    }				,      )






							def    A_      (   self      :	Optional[int]		) ->					List[Any]:





														"""simple docstring"""

														if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None):
																					raise ValueError(
																					    "--config_overrides can\'t be used in combination with --config_name or --model_name_or_path"		)


@dataclass
class 		lowerCamelCase  :







							"""simple docstring"""




							UpperCAmelCase_     		=       field(
							    default=a_				,      metadata={"help": "The name of the dataset to use (via the datasets library)."}     )
							UpperCAmelCase_     		=       field(
							    default=a_				,      metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}     )
							UpperCAmelCase_     		=       field(default=a_				,      metadata={"help": "The input training data file (a text file)."}     )
							UpperCAmelCase_     		=       field(
							    default=a_				,      metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."}				,      )
							UpperCAmelCase_     		=       field(
							    default=a_				,      metadata={"help": "An optional input train ref data file for whole word masking in Chinese."}				,      )
							UpperCAmelCase_     		=       field(
							    default=a_				,      metadata={"help": "An optional input validation ref data file for whole word masking in Chinese."}				,      )
							UpperCAmelCase_     		=       field(
							    default=a_				,      metadata={"help": "Overwrite the cached training and evaluation sets"}     )
							UpperCAmelCase_     		=       field(
							    default=5				,      metadata={
							        "help": "The percentage of the train set used as validation set in case there's no validation split"
							    }				,      )
							UpperCAmelCase_     		=       field(
							    default=a_				,      metadata={
							        "help": (
							            "The maximum total input sequence length after tokenization. Sequences longer "
							            "than this will be truncated. Default to the max input length of the model."
							        )
							    }				,      )
							UpperCAmelCase_     		=       field(
							    default=a_				,      metadata={"help": "The number of processes to use for the preprocessing."}				,      )
							UpperCAmelCase_     		=       field(
							    default=0.15				,      metadata={"help": "Ratio of tokens to mask for masked language modeling loss"}     )
							UpperCAmelCase_     		=       field(
							    default=a_				,      metadata={
							        "help": (
							            "Whether to pad all samples to `max_seq_length`. "
							            "If False, will pad the samples dynamically when batching to the maximum length in the batch."
							        )
							    }				,      )






							def    A_      (   self      :	List[str]		) ->					Union[str, Any]:





														"""simple docstring"""

														if self.train_file is not None:
																					SCREAMING_SNAKE_CASE__  :     List[Any]           =		self.train_file.split("."		)[-1]
																					assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
														if self.validation_file is not None:
																					SCREAMING_SNAKE_CASE__  :     Union[str, Any]           =		self.validation_file.split("."		)[-1]
																					assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."




def _a   (    SCREAMING_SNAKE_CASE__     :    Tuple					, SCREAMING_SNAKE_CASE__     :    str  )			->							Tuple:

							'''simple docstring'''






							with open(__snake_case					, "r"					, encoding="utf-8"  ) as f:
														SCREAMING_SNAKE_CASE__  :     Any           =		[json.loads(__snake_case  ) for line in f.read().splitlines() if (len(__snake_case  ) > 0 and not line.isspace())]
							assert len(__snake_case  ) == len(__snake_case  )

							SCREAMING_SNAKE_CASE__  :     Optional[Any]           =		{c: dataset[c] for c in dataset.column_names}
							SCREAMING_SNAKE_CASE__  :     Dict           =		refs
							return Dataset.from_dict(__snake_case  )




def _a   (    )			->							Union[str, Any]:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__  :     List[str]           =		HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)  )
							if len(sys.argv  ) == 2 and sys.argv[1].endswith(".json"  ):
														# If we pass only one argument to the script and it's the path to a json file,
														# let's parse it to get our arguments.
														SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__  :     List[str]           =		parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]  )  )
							else:
														SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__  :     Tuple           =		parser.parse_args_into_dataclasses()

							# Detecting last checkpoint.
							SCREAMING_SNAKE_CASE__  :     Tuple           =		None
							if os.path.isdir(training_args.output_dir  ) and training_args.do_train and not training_args.overwrite_output_dir:
														SCREAMING_SNAKE_CASE__  :     str           =		get_last_checkpoint(training_args.output_dir  )
														if last_checkpoint is None and len(os.listdir(training_args.output_dir  )  ) > 0:
																					raise ValueError(
																					    f'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
																					    "Use --overwrite_output_dir to overcome."  )
														elif last_checkpoint is not None:
																					logger.info(
																					    f'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
																					    "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."  )

    # Setup logging
							logging.basicConfig(
							    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"					, datefmt="%m/%d/%Y %H:%M:%S"					, handlers=[logging.StreamHandler(sys.stdout  )]					, )
							logger.setLevel(logging.INFO if is_main_process(training_args.local_rank  ) else logging.WARN  )

							# Log on each process the small summary:
							logger.warning(
							    f'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
							    + f'''distributed training: {bool(training_args.local_rank != -1  )}, 16-bits training: {training_args.fpaa}'''  )
							# Set the verbosity to info of the Transformers logger (on main process only):
							if is_main_process(training_args.local_rank  ):
														transformers.utils.logging.set_verbosity_info()
														transformers.utils.logging.enable_default_handler()
														transformers.utils.logging.enable_explicit_format()
							logger.info("Training/evaluation parameters %s"					, __snake_case  )

							# Set seed before initializing model.
							set_seed(training_args.seed  )

							# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
							# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
							# (the dataset will be downloaded automatically from the datasets Hub).
							#
							# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
							# 'text' is found. You can easily tweak this behavior (see below).
							#
							# In distributed training, the load_dataset function guarantee that only one local process can concurrently
							# download the dataset.
							if data_args.dataset_name is not None:
														# Downloading and loading a dataset from the hub.
														SCREAMING_SNAKE_CASE__  :     List[str]           =		load_dataset(data_args.dataset_name					, data_args.dataset_config_name  )
														if "validation" not in datasets.keys():
																					SCREAMING_SNAKE_CASE__  :     List[Any]           =		load_dataset(
																					    data_args.dataset_name					, data_args.dataset_config_name					, split=f'''train[:{data_args.validation_split_percentage}%]'''					, )
																					SCREAMING_SNAKE_CASE__  :     Optional[Any]           =		load_dataset(
																					    data_args.dataset_name					, data_args.dataset_config_name					, split=f'''train[{data_args.validation_split_percentage}%:]'''					, )
							else:
														SCREAMING_SNAKE_CASE__  :     Union[str, Any]           =		{}
														if data_args.train_file is not None:
																					SCREAMING_SNAKE_CASE__  :     str           =		data_args.train_file
														if data_args.validation_file is not None:
																					SCREAMING_SNAKE_CASE__  :     Union[str, Any]           =		data_args.validation_file
														SCREAMING_SNAKE_CASE__  :     str           =		data_args.train_file.split("."  )[-1]
														if extension == "txt":
																					SCREAMING_SNAKE_CASE__  :     Union[str, Any]           =		"text"
														SCREAMING_SNAKE_CASE__  :     Any           =		load_dataset(__snake_case					, data_files=__snake_case  )
							# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
							# https://huggingface.co/docs/datasets/loading_datasets.html.

							# Load pretrained model and tokenizer
							#
							# Distributed training:
							# The .from_pretrained methods guarantee that only one local process can concurrently
							# download model & vocab.
							SCREAMING_SNAKE_CASE__  :     Union[str, Any]           =		{
							    "cache_dir": model_args.cache_dir,
							    "revision": model_args.model_revision,
							    "use_auth_token": True if model_args.use_auth_token else None,
							}
							if model_args.config_name:
														SCREAMING_SNAKE_CASE__  :     Optional[int]           =		AutoConfig.from_pretrained(model_args.config_name					, **__snake_case  )
							elif model_args.model_name_or_path:
														SCREAMING_SNAKE_CASE__  :     Optional[int]           =		AutoConfig.from_pretrained(model_args.model_name_or_path					, **__snake_case  )
							else:
														SCREAMING_SNAKE_CASE__  :     Dict           =		CONFIG_MAPPING[model_args.model_type]()
														logger.warning("You are instantiating a new config instance from scratch."  )
														if model_args.config_overrides is not None:
																					logger.info(f'''Overriding config: {model_args.config_overrides}'''  )
																					config.update_from_string(model_args.config_overrides  )
																					logger.info(f'''New config: {config}'''  )

							SCREAMING_SNAKE_CASE__  :     Union[str, Any]           =		{
							    "cache_dir": model_args.cache_dir,
							    "use_fast": model_args.use_fast_tokenizer,
							    "revision": model_args.model_revision,
							    "use_auth_token": True if model_args.use_auth_token else None,
							}
							if model_args.tokenizer_name:
														SCREAMING_SNAKE_CASE__  :     List[str]           =		AutoTokenizer.from_pretrained(model_args.tokenizer_name					, **__snake_case  )
							elif model_args.model_name_or_path:
														SCREAMING_SNAKE_CASE__  :     Dict           =		AutoTokenizer.from_pretrained(model_args.model_name_or_path					, **__snake_case  )
							else:
														raise ValueError(
														    "You are instantiating a new tokenizer from scratch. This is not supported by this script."
														    "You can do it from another script, save it, and load it from here, using --tokenizer_name."  )

							if model_args.model_name_or_path:
														SCREAMING_SNAKE_CASE__  :     List[str]           =		AutoModelForMaskedLM.from_pretrained(
														    model_args.model_name_or_path					, from_tf=bool(".ckpt" in model_args.model_name_or_path  )					, config=__snake_case					, cache_dir=model_args.cache_dir					, revision=model_args.model_revision					, use_auth_token=True if model_args.use_auth_token else None					, )
							else:
														logger.info("Training new model from scratch"  )
														SCREAMING_SNAKE_CASE__  :     Optional[Any]           =		AutoModelForMaskedLM.from_config(__snake_case  )

							model.resize_token_embeddings(len(__snake_case  )  )

							# Preprocessing the datasets.
							# First we tokenize all the texts.
							if training_args.do_train:
														SCREAMING_SNAKE_CASE__  :     Optional[Any]           =		datasets["train"].column_names
							else:
														SCREAMING_SNAKE_CASE__  :     Any           =		datasets["validation"].column_names
							SCREAMING_SNAKE_CASE__  :     Dict           =		"text" if "text" in column_names else column_names[0]

							SCREAMING_SNAKE_CASE__  :     Union[str, Any]           =		"max_length" if data_args.pad_to_max_length else False

							def tokenize_function(SCREAMING_SNAKE_CASE__     :    str  ):
														# Remove empty lines
														SCREAMING_SNAKE_CASE__  :     Optional[Any]           =		[line for line in examples["text"] if len(__snake_case  ) > 0 and not line.isspace()]
														return tokenizer(examples["text"]					, padding=__snake_case					, truncation=__snake_case					, max_length=data_args.max_seq_length  )

							SCREAMING_SNAKE_CASE__  :     Optional[int]           =		datasets.map(
							    __snake_case					, batched=__snake_case					, num_proc=data_args.preprocessing_num_workers					, remove_columns=[text_column_name]					, load_from_cache_file=not data_args.overwrite_cache					, )

							# Add the chinese references if provided
							if data_args.train_ref_file is not None:
														SCREAMING_SNAKE_CASE__  :     Dict           =		add_chinese_references(tokenized_datasets["train"]					, data_args.train_ref_file  )
							if data_args.validation_ref_file is not None:
														SCREAMING_SNAKE_CASE__  :     Tuple           =		add_chinese_references(
														    tokenized_datasets["validation"]					, data_args.validation_ref_file  )
							# If we have ref files, need to avoid it removed by trainer
							SCREAMING_SNAKE_CASE__  :     str           =		data_args.train_ref_file or data_args.validation_ref_file
							if has_ref:
														SCREAMING_SNAKE_CASE__  :     Dict           =		False

							# Data collator
							# This one will take care of randomly masking the tokens.
							SCREAMING_SNAKE_CASE__  :     Optional[Any]           =		DataCollatorForWholeWordMask(tokenizer=__snake_case					, mlm_probability=data_args.mlm_probability  )

							# Initialize our Trainer
							SCREAMING_SNAKE_CASE__  :     Any           =		Trainer(
							    model=__snake_case					, args=__snake_case					, train_dataset=tokenized_datasets["train"] if training_args.do_train else None					, eval_dataset=tokenized_datasets["validation"] if training_args.do_eval else None					, tokenizer=__snake_case					, data_collator=__snake_case					, )

							# Training
							if training_args.do_train:
														if last_checkpoint is not None:
																					SCREAMING_SNAKE_CASE__  :     str           =		last_checkpoint
														elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path  ):
																					SCREAMING_SNAKE_CASE__  :     List[Any]           =		model_args.model_name_or_path
														else:
																					SCREAMING_SNAKE_CASE__  :     Optional[int]           =		None
														SCREAMING_SNAKE_CASE__  :     Any           =		trainer.train(resume_from_checkpoint=__snake_case  )
														trainer.save_model()  # Saves the tokenizer too for easy upload

														SCREAMING_SNAKE_CASE__  :     Union[str, Any]           =		os.path.join(training_args.output_dir					, "train_results.txt"  )
														if trainer.is_world_process_zero():
																					with open(__snake_case					, "w"  ) as writer:
																												logger.info("***** Train results *****"  )
																												for key, value in sorted(train_result.metrics.items()  ):
																																			logger.info(f'''  {key} = {value}'''  )
																																			writer.write(f'''{key} = {value}\n'''  )

            # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
																					trainer.state.save_to_json(os.path.join(training_args.output_dir					, "trainer_state.json"  )  )

    # Evaluation
							SCREAMING_SNAKE_CASE__  :     Union[str, Any]           =		{}
							if training_args.do_eval:
														logger.info("*** Evaluate ***"  )

														SCREAMING_SNAKE_CASE__  :     Tuple           =		trainer.evaluate()

														SCREAMING_SNAKE_CASE__  :     Dict           =		math.exp(eval_output["eval_loss"]  )
														SCREAMING_SNAKE_CASE__  :     str           =		perplexity

														SCREAMING_SNAKE_CASE__  :     Optional[Any]           =		os.path.join(training_args.output_dir					, "eval_results_mlm_wwm.txt"  )
														if trainer.is_world_process_zero():
																					with open(__snake_case					, "w"  ) as writer:
																												logger.info("***** Eval results *****"  )
																												for key, value in sorted(results.items()  ):
																																			logger.info(f'''  {key} = {value}'''  )
																																			writer.write(f'''{key} = {value}\n'''  )

							return results




def _a   (    SCREAMING_SNAKE_CASE__     :    List[str]  )			->							Union[str, Any]:

							'''simple docstring'''






							main()


if __name__ == "__main__":
		main()














"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
 from .tokenization_big_bird import BigBirdTokenizer
else:
 snake_case : Tuple     		=		None

snake_case : Tuple     		=		logging.get_logger(__name__)
snake_case : List[Any]     		=		{"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

snake_case : Dict     		=		{
    """vocab_file""": {
        """google/bigbird-roberta-base""": """https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model""",
        """google/bigbird-roberta-large""": (
            """https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"""
        ),
        """google/bigbird-base-trivia-itc""": (
            """https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"""
        ),
    },
    """tokenizer_file""": {
        """google/bigbird-roberta-base""": (
            """https://huggingface.co/google/bigbird-roberta-base/resolve/main/tokenizer.json"""
        ),
        """google/bigbird-roberta-large""": (
            """https://huggingface.co/google/bigbird-roberta-large/resolve/main/tokenizer.json"""
        ),
        """google/bigbird-base-trivia-itc""": (
            """https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/tokenizer.json"""
        ),
    },
}

snake_case : int     		=		{
    """google/bigbird-roberta-base""": 4_0_9_6,
    """google/bigbird-roberta-large""": 4_0_9_6,
    """google/bigbird-base-trivia-itc""": 4_0_9_6,
}


snake_case : List[str]     		=		"""▁"""



class        UpperCamelCase__ (				a_):







    """simple docstring"""
    __UpperCAmelCase           = VOCAB_FILES_NAMES
    __UpperCAmelCase           = PRETRAINED_VOCAB_FILES_MAP
    __UpperCAmelCase           = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __UpperCAmelCase           = BigBirdTokenizer
    __UpperCAmelCase           = ["""input_ids""", """attention_mask"""]
    __UpperCAmelCase           = []







    def __init__(		self :	Optional[int]				,    UpperCamelCase_ :	int=None				,    UpperCamelCase_ :	Dict=None				,    UpperCamelCase_ :	int="<unk>"				,    UpperCamelCase_ :	Tuple="<s>"				,    UpperCamelCase_ :	int="</s>"				,    UpperCamelCase_ :	Union[str, Any]="<pad>"				,    UpperCamelCase_ :	Optional[Any]="[SEP]"				,    UpperCamelCase_ :	int="[MASK]"				,    UpperCamelCase_ :	Tuple="[CLS]"				,    **UpperCamelCase_ :	Optional[Any]				,    ):





           '''simple docstring'''

           __magic_name__      				=    AddedToken(UpperCamelCase_				,    lstrip=UpperCamelCase_				,    rstrip=UpperCamelCase_ ) if isinstance(UpperCamelCase_				,    UpperCamelCase_ ) else bos_token
           __magic_name__      				=    AddedToken(UpperCamelCase_				,    lstrip=UpperCamelCase_				,    rstrip=UpperCamelCase_ ) if isinstance(UpperCamelCase_				,    UpperCamelCase_ ) else eos_token
           __magic_name__      				=    AddedToken(UpperCamelCase_				,    lstrip=UpperCamelCase_				,    rstrip=UpperCamelCase_ ) if isinstance(UpperCamelCase_				,    UpperCamelCase_ ) else unk_token
           __magic_name__      				=    AddedToken(UpperCamelCase_				,    lstrip=UpperCamelCase_				,    rstrip=UpperCamelCase_ ) if isinstance(UpperCamelCase_				,    UpperCamelCase_ ) else pad_token
           __magic_name__      				=    AddedToken(UpperCamelCase_				,    lstrip=UpperCamelCase_				,    rstrip=UpperCamelCase_ ) if isinstance(UpperCamelCase_				,    UpperCamelCase_ ) else cls_token
           __magic_name__      				=    AddedToken(UpperCamelCase_				,    lstrip=UpperCamelCase_				,    rstrip=UpperCamelCase_ ) if isinstance(UpperCamelCase_				,    UpperCamelCase_ ) else sep_token

           # Mask token behave like a normal word, i.e. include the space before it
           __magic_name__      				=    AddedToken(UpperCamelCase_				,    lstrip=UpperCamelCase_				,    rstrip=UpperCamelCase_ ) if isinstance(UpperCamelCase_				,    UpperCamelCase_ ) else mask_token

           super().__init__(
               UpperCamelCase_				,    tokenizer_file=UpperCamelCase_				,    bos_token=UpperCamelCase_				,    eos_token=UpperCamelCase_				,    unk_token=UpperCamelCase_				,    sep_token=UpperCamelCase_				,    pad_token=UpperCamelCase_				,    cls_token=UpperCamelCase_				,    mask_token=UpperCamelCase_				,    **UpperCamelCase_				,    )

           __magic_name__      				=    vocab_file
           __magic_name__      				=    False if not self.vocab_file else True







    def 				a__			(		self :	Union[str, Any]				,    UpperCamelCase_ :	List[int]				,    UpperCamelCase_ :	Optional[List[int]] = None ):





           '''simple docstring'''

           __magic_name__      				=    [self.sep_token_id]
           __magic_name__      				=    [self.cls_token_id]
           if token_ids_a is None:
                  return cls + token_ids_a + sep
           return cls + token_ids_a + sep + token_ids_a + sep







    def 				a__			(		self :	Optional[Any]				,    UpperCamelCase_ :	List[int]				,    UpperCamelCase_ :	Optional[List[int]] = None				,    UpperCamelCase_ :	bool = False ):





           '''simple docstring'''

           if already_has_special_tokens:
                  if token_ids_a is not None:
                         raise ValueError(
                             'You should not supply a second sequence if the provided sequence of '
                             'ids is already formatted with special tokens for the model.' )
                  return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

           if token_ids_a is None:
                  return [1] + ([0] * len(UpperCamelCase_ )) + [1]
           return [1] + ([0] * len(UpperCamelCase_ )) + [1] + ([0] * len(UpperCamelCase_ )) + [1]







    def 				a__			(		self :	Optional[Any]				,    UpperCamelCase_ :	List[int]				,    UpperCamelCase_ :	Optional[List[int]] = None ):





           '''simple docstring'''

           __magic_name__      				=    [self.sep_token_id]
           __magic_name__      				=    [self.cls_token_id]

           if token_ids_a is None:
                  return len(cls + token_ids_a + sep ) * [0]
           return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]







    def 				a__			(		self :	Union[str, Any]				,    UpperCamelCase_ :	str				,    UpperCamelCase_ :	Optional[str] = None ):





           '''simple docstring'''

           if not self.can_save_slow_tokenizer:
                  raise ValueError(
                      'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
                      'tokenizer.' )

           if not os.path.isdir(UpperCamelCase_ ):
                  logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
                  return
           __magic_name__      				=    os.path.join(
               UpperCamelCase_				,    (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )

           if os.path.abspath(self.vocab_file ) != os.path.abspath(UpperCamelCase_ ):
                  copyfile(self.vocab_file				,    UpperCamelCase_ )

           return (out_vocab_file,)